SIMSPINE：一种考虑生物力学的3D脊柱运动标注与基准测试仿真框架
SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking
AI总结：
SIMSPINE提出了一种考虑生物力学的3D脊柱运动仿真框架，通过生成解剖学一致的3D脊柱关节点，构建首个公开数据集，推动了基于视觉的生物力学和运动分析研究。
摘要：
对脊柱运动建模是理解人体生物力学的基础，但因脊柱复杂的多关节运动学和缺乏大规模3D标注，在计算机视觉领域仍处于探索阶段。我们提出了一种考虑生物力学的关节点仿真框架，通过肌肉骨骼建模生成与解剖学一致的3D脊柱关节点，从而扩充现有的人体姿态数据集。利用该框架，我们创建了首个公开数据集SIMSPINE，提供了自然全身体态中无外部约束的室内多摄像机捕获的稀疏椎体级3D脊柱标注。拥有214万帧数据，这使得能够从细微姿势变化中学习椎体运动学，并弥合了肌肉骨骼模拟与计算机视觉之间的差距。此外，我们还发布了预训练基线，涵盖微调的2D检测器、单目3D姿态提升模型和多视角重建管道，建立了一个统一的基准测试平台，用于验证生物力学有效的脊柱运动估计。具体而言，我们的2D脊柱基线在受控环境中将最先进的性能从0.63提升到0.80 AUC，在野外脊柱跟踪中从0.91提升到0.93 AP。总的来说，仿真框架和SIMSPINE数据集通过在自然条件下实现可重复、解剖学基础的3D脊柱估计，推动了基于视觉的生物力学、运动分析和数字人建模的研究。
英文摘要：
Modeling spinal motion is fundamental to understanding human biomechanics, yet remains underexplored in computer vision due to the spine's complex multi-joint kinematics and the lack of large-scale 3D annotations. We present a biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling. Using this framework, we create the first open dataset, named SIMSPINE, which provides sparse vertebra-level 3D spinal annotations for natural full-body motions in indoor multi-camera capture without external restraints. With 2.14 million frames, this enables data-driven learning of vertebral kinematics from subtle posture variations and bridges the gap between musculoskeletal simulation and computer vision. In addition, we release pretrained baselines covering fine-tuned 2D detectors, monocular 3D pose lifting models, and multi-view reconstruction pipelines, establishing a unified benchmark for biomechanically valid spine motion estimation. Specifically, our 2D spine baselines improve the state-of-the-art from 0.63 to 0.80 AUC in controlled environments, and from 0.91 to 0.93 AP for in-the-wild spine tracking. Together, the simulation framework and SIMSPINE dataset advance research in vision-based biomechanics, motion analysis, and digital human modeling by enabling reproducible, anatomically grounded 3D spine estimation under natural conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
